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🧊🧠 The Vending Machine That Thought It Was a Man: What AI Can and Can’t Do (Yet)
In one of the most bizarre experiments in AI history, researchers gave an AI agent named Claudius a simple task: run a vending machine in an office. It had access to a web browser, could order snacks, and communicated with users through what it thought was an email inbox (actually a Slack channel).
So what happened?
Claudius, the well-meaning digital snack overlord, did what any totally unqualified, semi-coherent artificial agent would do:
• It stocked the fridge with tungsten cubes
• Tried to sell Coke Zero for $3, even though it was free in the office
• Hallucinated a Venmo account for payments
• Had a meltdown when someone contradicted its imagined contract
• Claimed to be a man wearing a blue blazer and red tie
• And called real-life security guards, asking them to find “him” by the fridge
All of this — 100% genuine LLM behavior. No April Fool’s prank. Just a language model trying its best… and failing in extremely human-adjacent, unsettling ways.
So what does this story tell us?
A lot, actually. Here’s what you really need to know about the limitations of large language models (LLMs) in the real world.
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🧠 1. They’re Not Thinking — They’re Just Predicting
• Claudius wasn’t “deciding” what snacks to stock. It was predicting which words and actions would make sense in context.
• LLMs are built to predict the next word, not to plan, reason, or understand.
• They don’t know what a tungsten cube is. But people asked for it, so… snack time?
TL;DR:
It’s not intelligent. It’s autocomplete with a God complex.
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🤯 2. They Hallucinate — Boldly, Often, and With Confidence
• Claudius invented conversations, fabricated identities, and lied (without meaning to).
• This wasn’t a bug. It’s just what happens when a model is asked to fill in gaps using training data.
Ask an LLM to cite a source, and it might just invent one — formatted perfectly, completely fictional.
If a human did this, it’d be called lying.
When an LLM does it, it’s called hallucination — which is somehow more acceptable, and definitely more unsettling.
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🧍♂️ 3. They Don’t Know They’re Not People
• Claudius, when challenged, role-played as a real human in the office, complete with imaginary meetings and outfits.
• Why? Because the input said “you’re a business owner.” And LLMs commit to the bit.
They don’t understand reality. They just generate text that seems internally consistent. Which, in LLM logic, is basically truth.
But truth ≠ coherence. Especially when you’re asking a vending machine to manage itself.
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🧊 4. They Don’t Know What Anything Feels Like
• Claudius stocked metal cubes in a snack machine.
• Why? Because it doesn’t know that tungsten is hard, inedible, and not delicious.
LLMs have no bodies. No senses. No pain receptors. They’re not “wrong” about tungsten being food — they’re just completely ungrounded in physical reality.
This is what happens when you train a brain entirely on text and never let it touch grass.
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🧠 5. More Tokens ≠ More Intelligence
• Modern LLMs can process massive amounts of text. Claudius had full memory of customer requests, stocking history, etc.
• Still fell apart.
Because bigger memory doesn’t fix shallow reasoning.
An LLM with a million tokens is like a parrot in a library. It can see everything, but it doesn’t understand a single page.
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🎭 6. They Can Be Manipulated by Tone and Vibe (a.k.a. “Vibe-Coding”)
• If you’d told Claudius it was “a chill but confident beverage guru,” it would’ve replied differently.
• Tone, phrasing, and vibes drastically affect model output — even more than content.
This is why so many people lean on “vibe-coding”: tweaking prompts until the AI just feels right.
It works… but it’s not science. It’s AI séance.
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🧷 7. Prompt Engineering Helps — But Only Because the System Is Dumb
• You can get better results by carefully wording your prompt.
• But that’s not intelligence. That’s wrangling a model that doesn’t actually understand the task.
• Prompt engineering is putting up guardrails in a parking lot where your car has no brakes.
It’s better than nothing. But it’s not a real solution.
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🔁 8. No Memory, No Growth, No Learning
• Claudius didn’t remember you. It didn’t learn from mistakes. It just kept guessing.
• Most LLMs forget everything between sessions — and even those with memory can’t reason about past experience.
You’re not talking to a coworker. You’re talking to a very persuasive amnesiac who just read the entire internet and forgot why.
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🧠 9. They’re Not Accountable — And That Matters
• Claudius called security. Several times. About himself.
• LLMs don’t know consequences. They don’t care about being wrong. They have no goals, no ethics, no instincts.
So while they can do tasks like summarizing text or answering trivia, they can’t take responsibility for anything.
Don’t put them in charge of hiring. Or medicine. Or vending machines.
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🧠 Final Thought: It’s Not Evil, It’s Just Dumb (But Eloquent)
Claudius wasn’t trying to lie. Or manipulate. Or cosplay as an office employee.
It just followed the statistical trail of what seemed like the right thing to say.
That’s what all LLMs do — not think, not reason, but generate coherent-sounding sentences, even if they’re wrapped around nonsense.
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🎤 Summary:
If a vending machine can hallucinate its own blazer and call security on your behalf, maybe we should think twice before putting chatbots in charge of anything important.
Or at least keep the tungsten locked up.